Federated learning for predicting clinical outcomes in patients with COVID-19.

@article{Dayan2021FederatedLF,
  title={Federated learning for predicting clinical outcomes in patients with COVID-19.},
  author={Ittai Dayan and Holger R. Roth and Aoxiao Zhong and Ahmed El Harouni and Amilcare Gentili and Anas Zainul Abidin and Andrew Liu and Anthony Beardsworth Costa and Bradford J. Wood and Chien-Sung Tsai and Chih-Hung Wang and Chun-Nan Hsu and C K Lee and Peiying Ruan and Daguang Xu and Dufan Wu and Eddie Huang and Felipe Campos Kitamura and Griffin Lacey and Gustavo C{\'e}sar de Ant{\^o}nio Corradi and Gustavo Nino and Hao-Hsin Shin and Hirofumi Obinata and Hui Ren and Jason C. Crane and Jesse Tetreault and Jiahui Guan and John W. Garrett and Joshua D. Kaggie and Jung Gil Park and Keith Dreyer and Krishna Juluru and Kristopher Kersten and Marcio Aloisio Bezerra Cavalcanti Rockenbach and Marius George Linguraru and Masoom A. Haider and Meena Abdelmaseeh and Nicola Rieke and Pablo F. Damasceno and Pedro M{\'a}rio Cruz e Silva and Pochuan Wang and Sheng Xu and Shuichi Kawano and Sira Sriswasdi and Soo Young Park and Thomas M. Grist and Varun Buch and Watsamon Jantarabenjakul and Weichung Wang and Won Young Tak and Xiang Li and Xihong Lin and Young Joon Kwon and Abood Quraini and Andrew Feng and Andrew N. Priest and Baris I Turkbey and Benjamin Scott Glicksberg and Bernardo Canedo Bizzo and Byung Seok Kim and Carlos Tor-D{\'i}ez and Chia-Cheng Lee and Chia-Jung Hsu and Chin Lin and Chiu-Ling Lai and Christopher P. Hess and Colin B. Compas and Deepeksha Bhatia and Eric Karl Oermann and Evan Leibovitz and Hisashi Sasaki and Hitoshi Mori and Isaac Yang and Jae Ho Sohn and Krishna Nand Keshava Murthy and Lijuan Fu and Matheus Ribeiro Furtado de Mendonça and Michael Fralick and Min Kyu Kang and Mohammad Adil and Natalie Gangai and Peerapon Vateekul and Pierre Elnajjar and Sarah E Hickman and Sharmila Majumdar and Shelley L. McLeod and Sheridan L Reed and Stefan Gr{\"a}f and Stephanie A. Harmon and Tatsuya Kodama and Thanyawee Puthanakit and Tony Mazzulli and Vitor Lima de Lavor and Yothin Rakvongthai and Yu Rim Lee and Yuhong Wen and Fiona J. Gilbert and Mona G. Flores and Quanzheng Li},
  journal={Nature medicine},
  year={2021}
}
Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved… 

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